Recent years have seen the proliferation of wearable devices such as smartwatches and wristbands in the consumer electronics market. Wearable devices make computing technology pervasive by interweaving it into users' daily lives. These wearable devices generally allow users to track their fitness, activities, health and/or well-being, through the use of electronics, software, and sensors in those devices.
Existing wearable devices are typically geared towards improvement of users' fitness and well-being. Additionally, the use of wearable devices can be extended to other areas such as healthcare monitoring. Although wearable devices are capable of collecting large volumes of data about users, there is presently a lack of systems and algorithms that can accurately and efficiently analyze large volumes of data in certain healthcare areas. Examples of those healthcare areas may include monitoring of smoking behavior (e.g., smoking cessation), monitoring of certain types of eating and/or drinking disorders, monitoring of certain types of obsessive compulsive disorders, or monitoring of certain types of neurological diseases that display symptoms associated with repetitive vibration or shaking of a person's hands. Each of the above behaviors may be characterized by different and frequent ‘hand-to-mouth’ gestures. Existing systems and algorithms often lack the capability to accurately detect and monitor those gestures in real-time.
Thus, there is a need for methods and systems that can accurately detect and monitor various user gestures in real-time, and deliver relevant and personalized information to users in a timely manner to help them manage certain behaviors and habits, thus helping them improve their lives in a step-wise fashion.